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Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4-Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn.

Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux (H). We evaluated aerodynamic resistance and surface roughness lengths for momentum (z0m) and heat (z0h) using the kB−1 parameter (ln(z0m/z0h)). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer (Rah). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with Rah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in Rah were not linked to green-up. Our findings highlight that decreases in kB−1 are an important control over Rah, explaining > 50% of seasonal variation in Rah across most sites. Decreases in kB−1 during green-up are likely caused by increasing z0h in response to higher leaf area index. Accounting for seasonal variation in kB−1 is key for predicting H as well; assuming kB−1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.

Tropical peatlands are a major, but understudied, biophysical feedback factor on the atmospheric greenhouse effect. The largest expanses of tropical peatlands are located in lowland areas of Southeast Asia and the Amazon basin. The Loreto Region of Amazonian Peru contains ~63,000 km2 of peatlands. However, little is known about the biogeochemistry of these peatlands, and in particular, the cycling of carbon dioxide (CO2) and methane (CH4), and their responses to hydrometeorological forcings. To address these knowledge gaps, we established an eddy covariance (EC) flux tower in a natural palm (Mauritia flexuosa L.f.) swamp peatland near Iquitos, Peru. Here, we report ecosystem-scale CO2 and CH4 flux observations for this Amazonian palm swamp peatland over a two-year period in relation to hydrometeorological forcings. Seasonal and short-term variations in hydrometeorological forcing had a strong effect on CO2 and CH4 fluxes. High air temperature and vapor pressure deficit (VPD) exerted an important limitation on photosynthesis during the dry season, while latent heat flux appeared to be insensitive to these climate drivers. Evidence from light-response analyses and flux partitioning support that photosynthetic activity was downregulated during dry conditions, while ecosystem respiration (RE) was either inhibited or enhanced depending on water table position. The cumulative net ecosystem CO2 exchange indicated that the peatland was a significant CO2 sink ranging from −465 (−279 to −651) g C m−2 y−1 in 2018 to −462 (−277 to −647) g C m−2 y−1 in 2019. The forest was a CH4 source of 22 (20 to 24) g C m−2 y−1, similar in magnitude to other tropical peatlands and larger than boreal and arctic peatlands. Thus, the annual carbon budget of this Amazonian palm swamp peatland appears to be a major carbon sink under current hydrometeorological conditions.

All-black thermopile pyranometers are commonly used to measure solar radiation. Ensuring that the sensors are stable and free of drift is critical to accurately measure small variations in global solar irradiance at the Earth’s surface (K↓), which is a potential driver of changes in surface temperature. We demonstrate that the decreased responsivities of Eppley PSP pyranometers of −1.5% y−1, or −0.38% (GJ m−2)−1, were accompanied by a change in its spectral response owing to a discoloration of the sensing element. These observations motivated further work to develop routines to detect probable pyranometer drift in historical time-series. The temporal trends in the following ratios were used to detect pyranometer sensor drift: photosynthetically active radiation (PAR) to K↓, K↓ to KEX (extraterrestrial radiation at the top of the atmosphere) and PAR to KEX. Data from 8 AmeriFlux sites spanning latitudes from ∼32 to 54°N were examined in this analysis. Probable drift in either a pyranometer or PAR sensor was identified at 5 of the 8 sites. The magnitude of the drift represented changes of 0.15–0.85% y−1, which is sufficient to obscure actual trends in K↓, although these should be considered conservative low end drift estimates, given that we were not making comparisons to co-located higher grade instruments. Deployment exposure errors caused by sensor shading were also discovered by comparing the daily correlations between (i) K↓ and KEX and (ii) PAR and KEX. Sensors drifting at rates similar to our defective PSP over a 5 year period would contribute to an underestimation of available energy of ∼70 W m−2, which is non-trivial in the context of assessing eddy covariance energy balance closure, employing Penman-Monteith or Bowen ratio methods or calculating albedo radiative forcings. Given that probable drift was identified at multiple AmeriFlux sites, we recommend enhancing network access to calibration services that are traceable to a high quality gold standard.

Wetlands represent the dominant natural source of methane (CH(4)) to the atmosphere. Thus, substantial effort has been spent examining the CH(4) budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH(4) fluxes using a 3.5 year time series of simultaneous open- and closed path CH(4) flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH(4) flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH(4) fluxes were typically 6%–41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH(4) fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g − CH(4) m(−2) yr(−1)). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH(4) emissions for both EC systems were in excellent agreement (within 0.6 g − CH(4) m(−2) yr(−1)) and averaged 18 g − CH(4) m(−2) yr(−1). Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7–17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Manure slurry storage systems are possible sources of methane (CH4) and nitrous oxide (N2O), both of which are strong greenhouse gases (GHG). The most commonly used methods to measure these emissions rely on chamber techniques deployed at discrete intervals (i.e. non-continuously). Due to long-term and diurnal variations in GHG emissions, discrete sampling may yield biased estimates when integrating fluxes over time. This research quantified the effect of sampling interval and the ‘time-of-day’ of sample collection on total emission estimates from discrete sampling compared to continuous monitoring, and characterized temporal flux variations in relation to environmental conditions. Methane and N2O emissions were measured continuously over the 6 mo warm storage season in 2010 from 6 pilot-scale dairy manure slurry storage tanks using flow-through steady state chambers. Discrete sampling was simulated by extracting data from hourly flux time series at 13 sampling intervals ranging from 1 to 21 d. For each sampling interval, 24 datasets were generated, one for each hour of the day. When there were high rates of CH4 ebullition, and crusts did not completely cover the surface, diurnal flux variations were linked with the diurnal surface temperature (T0) cycle, whereby bubbles that accumulated at the surface overnight burst after sunrise. With complete manure surface crusting, episodic and unpredictable flux events were more common. Diurnal variation in N2O emissions was strongly linked with variation in T0 when crusts >2 mo old were present. For CH4, sampling between 1800 and 0800 h at intervals ≤7 d yielded ±10% deviation between discrete and continuous monitoring with a frequency of 96%. The frequency of achieving ±10% deviation for N2O was 50% when sampling at ∼2000 h. If sampling during these times is not possible, manual measurements should be made in early morning (before 0900 h) and late afternoon (after 1700 h).